Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs
Most methods for solving stiff systems of ordinary differential equations (ODEs) are based on backward differentiation formulas (BDFs) which normally require repeated solution of systems of linear equations with coefficient matrix, IhbJ,where J is the Jacobian matrix as part of a Newton-like iterati...
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my.upm.eprints.76682014-04-18T05:11:21Z http://psasir.upm.edu.my/id/eprint/7668/ Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs Othman, Khairil Iskandar Ibrahim, Zarina Bibi Suleiman, Mohamed Majid, Zanariah Most methods for solving stiff systems of ordinary differential equations (ODEs) are based on backward differentiation formulas (BDFs) which normally require repeated solution of systems of linear equations with coefficient matrix, IhbJ,where J is the Jacobian matrix as part of a Newton-like iteration on each time step. The matrix operations in the iteration scheme consumes a considerable amount of computational effort. Therefore, in this paper, our objective is to reduce the cost of the iteration scheme by technique of partitioning. The strategy adopted for partitioning is based on block Adams method and block BDF method. Numerical results demonstrates the efficiency of the proposed partitioning in improving both the accuracy and CPU time over traditional stiff methods. Elsevier 2007 Article PeerReviewed Othman, Khairil Iskandar and Ibrahim, Zarina Bibi and Suleiman, Mohamed and Majid, Zanariah (2007) Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs. Applied Mathematics and Computation, 188 (1). pp. 1642-1646. ISSN 0096-3003 http://dx.doi.org/10.1016/j.amc.2006.11.020 10.1016/j.amc.2006.11.020 English |
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Most methods for solving stiff systems of ordinary differential equations (ODEs) are based on backward differentiation formulas (BDFs) which normally require repeated solution of systems of linear equations with coefficient matrix, IhbJ,where J is the Jacobian matrix as part of a Newton-like iteration on each time step. The matrix operations in the iteration scheme consumes a considerable amount of computational effort. Therefore, in this paper, our objective is to reduce the cost of the iteration scheme by technique of partitioning. The strategy adopted for partitioning is based on block Adams
method and block BDF method. Numerical results demonstrates the efficiency of the proposed partitioning in improving
both the accuracy and CPU time over traditional stiff methods. |
format |
Article |
author |
Othman, Khairil Iskandar Ibrahim, Zarina Bibi Suleiman, Mohamed Majid, Zanariah |
spellingShingle |
Othman, Khairil Iskandar Ibrahim, Zarina Bibi Suleiman, Mohamed Majid, Zanariah Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
author_facet |
Othman, Khairil Iskandar Ibrahim, Zarina Bibi Suleiman, Mohamed Majid, Zanariah |
author_sort |
Othman, Khairil Iskandar |
title |
Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
title_short |
Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
title_full |
Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
title_fullStr |
Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
title_full_unstemmed |
Automatic intervalwise block partitioning using Adams type method and backward differentiation formula for solving ODEs |
title_sort |
automatic intervalwise block partitioning using adams type method and backward differentiation formula for solving odes |
publisher |
Elsevier |
publishDate |
2007 |
url |
http://psasir.upm.edu.my/id/eprint/7668/ http://dx.doi.org/10.1016/j.amc.2006.11.020 |
_version_ |
1643823791878438912 |
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13.250246 |